A Dive Into Regression Analysis In Data Science

Introduction to Regression Analysis

Regression analysis is a form of predictive modelling technique that investigates and models the relationship between a dependent (target) and independent variable(s) (predictors). The goal of regression analysis is to identify how changes in the independent variables impact the dependent variable. Now, let's dive into this using Python.

Implementation in Python

Python, being a versatile language, has libraries that can help us implement regression analysis with ease. For this blog post, we will be using two main libraries: Pandas for data handling and Sci-Kit Learn for implementing the Linear Regression model.

Step 1: Importing Libraries

import pandas as pd
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LinearRegression
from sklearn import metrics

Step 2: Load and Examine the Data

Now, let's load a dataset. We will use a simple built-in dataset about Boston Housing prices from the sklearn.datasets module for this example.

from sklearn.datasets import load_boston

boston = load_boston()

# Transform data into a dataframe
data = pd.DataFrame(boston.data)

Step 3: Train Test Split

Next, we divide our dataset into training and test datasets.

X = data.drop('Price', axis = 1)
y = data['Price']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Step 4: Apply Linear Regression

Now, let's apply the linear regression model provided by Sci-kit Learn.

model = LinearRegression()  
model.fit(X_train, y_train)

Step 5: Make Predictions

Once the model is trained, we can use it to make predictions and compare those with the actual values.

y_pred = model.predict(X_test)

df = pd.DataFrame({'Actual': y_test, 'Predicted': y_pred})
print(df)

Step 6: Evaluate the Model

Finally, we need to evaluate the performance of the model.

print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  
print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  
print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

Conclusion

Regression analysis, while straightforward, remains a powerful tool in the predictive modeling toolbox. Understanding the link between dependent and independent variables can aid in decision-making and prediction-making processes across a variety of fields. Happy exploring!.